(mobo import Concept___Events_With_Academic_Fields-migrated) |
(mobo import Concept___Events-migrated) |
||
| Line 5: | Line 5: | ||
|Homepage=icdm2016.eurecat.org/ | |Homepage=icdm2016.eurecat.org/ | ||
|City=Barcelona | |City=Barcelona | ||
| − | |Country= | + | |Country=Country:ES |
|Submission deadline=2016/06/17 | |Submission deadline=2016/06/17 | ||
|Submitted papers=904 | |Submitted papers=904 | ||
| Line 18: | Line 18: | ||
|End Date=2016/12/15 | |End Date=2016/12/15 | ||
|Academic Field=Data Mining | |Academic Field=Data Mining | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
}} | }} | ||
The IEEE International Conference on Data Mining series (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels. | The IEEE International Conference on Data Mining series (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels. | ||
Revision as of 14:18, 6 September 2022
The IEEE International Conference on Data Mining series (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference features workshops, tutorials, panels.
Topics of interest include, but are not limited to:
- Foundations, algorithms, models and theory of data mining
- Machine learning and statistical methods for data mining
- Mining text, semi-structured, spatio-temporal, streaming, graph, web, multimedia data
- Data mining systems and platforms, their efficiency, scalability and privacy
- Data mining in modeling, visualization, personalization and recommendation
- Applications of data mining in all domains including social, web, bioinformatics and finance
